The present invention relates to an Automatic Speech Recognition (ASR) system and method for recognizing and tagging a multitude of spoken strings of words, hereinafter known as utterances, as a single meaning within a natural language application. More particularly, and not by way of limitation, the present invention is directed to a system and method for assigning meaning to utterances such that groups of utterances may be tagged simultaneously.
In the related art, the creation and success of a natural language application may be tied to a speech recognition system's ability to understand a large list of transcribed utterances or sentences. The system must apply a complex set of rules referred to as semantic interpretation grammar. This grammar is based on a corpus which is tagged (a corpus is a collection of recorded utterances used for linguistic analysis). Therefore, before the grammar can be developed, each line in the corpus must be tagged. The Tagging process is performed by a subject matter expert (SME) who indicates the specific meaning of a sentence. For example, the sentence “I just received my bill today” could indicate “I want to pay my bill” in a bill payment context. In this example, the sentence would be tagged as “PAYBILL”.
The Tagging process requires significant effort from the SME and grammar developers (together, commonly referred to as Taggers) primarily related to the mapping of utterances to specific meanings (semantics). As part of this effort, the Tagging process requires review of all possible combinations of utterances and assignment to a specific semantic upon which a natural language application can react.
The sample size of potential utterances that must be tagged can sometimes be too large for Taggers to process. Further, limiting the Tagging process to a single Tagger can lead to excessive time consumed in the Tagging process and/or an insufficient mapping between potential utterances and their associated semantic.
Therefore, what is needed is a collaborative solution that allows assigning meaning to utterances such that groups of utterances may be tagged simultaneously, thereby improving efficiency in the Tagging process.